Project Summary/Abstract The mammalian brain is composed of a diverse array of neuronal and glial cell types that differ in their morphology, electrical characteristics, and molecular markers. Understanding the neural basis of behavior in health and disease will, therefore, require elucidation of the respective roles of these cell types in representing and processing information. However, traditional electrophysiological (electrode-based) methods for monitoring neural activity cannot be restricted to specific cell types. The solution suggested in this proposal is to develop improved Genetically Encoded Voltage Indicators (GEVIs), which are fluorescent proteins whose brightness is modulated by electrical activity (voltage). GEVIs are promising tools as their expression can be restricted to genetically-defined cell populations. Moreover, by directly reporting voltage rather than a surrogate parameter (e.g. calcium), they can monitor a richer repertoire of neuronal electrical signals, including inhibitory signals (hyperpolarizations), subthreshold depolarizations, and rapid trains of action potentials. While GEVIs have shown early promise for optical detection of voltage transients, their performance is currently insufficient for single-trial imaging of voltage in populations of individual neurons in vivo. This proposal aims to address this performance gap using a multipronged approach. First, the authors propose to optimize a new GEVI architecture optimized for long-term in vivo imaging. Second, natural diversity will be mined to uncover novel voltage-sensing proteins that enable detection of voltage dynamics with more sensitivity or temporal precision. Finally, a new high-throughput screening platform optimized for engineering GEVIs will be deployed for rapidly screening large libraries of indicators. To facilitate deployment in downstream applications, GEVIs with improved properties will be comprehensively evaluated across all key performance metrics in neurons, in brain slices, and in vivo. Overall, the experiments are anticipated to lead to designer GEVIs of broad utility and impact for understanding how the various cell types in the brain cooperate to enable behavior.